Single-channel surface electromyography signal classification with variational mode decomposition and entropy feature for lower limb movements recognition

Abstract

Currently, many researchers tend to use multi-channel surface electromyography (sEMG) signals to improve the accuracy of lower limb movement recognition. However, the collection of multi-channel sEMG signals will reduce the usability of wearable devices for lower limbs based on sEMG signals in amputees, patients with impaired muscle function, and the disabled. How to effectively use single-channel sEMG signals to achieve better recognition performance is a difficult problem to improve the usability of wearable devices based on sEMG signals. In this research, we proposed a precise feature extraction method for single-channel sEMG signals to achieve accurate recognition of lower limb movements. The single-channel sEMG signal was decomposed into multiple variational modal functions (VMF) through variational mode decomposition (VMD), and entropy features were extracted from VMFs to highlight the prominent information of the sEMG signal. Entropy features with statistical differences were selected by the Kruskal-Wallis test. Four lower limb movements were recognized through machine learning. Moreover, the recognition performance exhibited by the proposed method on the sEMG signal of two different muscles was evaluated. The sEMG signals of four lower limb movements from twenty subjects recorded by the wearable sEMG signal sensor were employed to test the proposed method. The experimental results showed that the accuracy of the proposed method for the sEMG signals of two different muscles reached 95.82% and 97.44%. This research concluded that the proposed method is promising to improve the usability of wearable devices based on sEMG signals in amputees, patients with impaired muscle function, and the disabled.